Teaching-learning based optimization with global crossover for global optimization problems

Teaching learning based optimization (TLBO) is a newly developed population-based meta-heuristic algorithm. It has better global searching capability but it also easily got stuck on local optima when solving global optimization problems. This paper develops a new variant of TLBO, called teaching learning based optimization with global crossover (TLBO-GC), for improving the performance of TLBO. In teaching phase, a perturbed scheme is proposed to prevent the current best solution from getting trapped in local minima. And a new global crossover strategy is incorporated into the learning phase, which aims at balancing local and global searching effectively. The performance of TLBO-GC is assessed by solving global optimization functions with different characteristics. Compared to the TLBO, several modified TLBOs and other promising heuristic methods, numerical results reveal that the TLBO-GC has better optimization performance.